Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
CIRCSIM-Tutor: an intelligent tutoring system using natural language dialogue
ANLC '97 Proceedings of the fifth conference on Applied natural language processing: Descriptions of system demonstrations and videos
Robust translation of spontaneous speech: a multi-engine approach
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Some useful tactics to modify, map and mine data from intelligent tutors
Natural Language Engineering
Spoken Versus Typed Human and Computer Dialogue Tutoring
International Journal of Artificial Intelligence in Education
The Effect of Explaining on Learning: a Case Study with a Data Normalization Tutor
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Determining tutorial remediation strategies from a corpus of human-human tutoring dialogues
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Interpretation and generation in a knowledge-based tutorial system
KRAQ '06 Proceedings of the Workshop KRAQ'06 on Knowledge and Reasoning for Language Processing
Natural language dialog with a tutor system for mathematical proofs
Proceedings of the 2005 joint Chinese-German conference on Cognitive systems
EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
Automatic evaluation of learner self-explanations and erroneous responses for dialogue-based ITSs
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Towards effective tutorial feedback for explanation questions: a dataset and baselines
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Previous studies have shown that self-explanation is an effective metacognitive strategy and can be supported effectively by intelligent tutoring systems. It is plausible however that students may learn even more effectively when stating explanations in their own words and when receiving tutoring focused on their explanations. We are developing the Geometry Explanation Tutor in order to test this hypothesis. This system helps students, through a restricted form of dialogue, to construct general explanations of problem-solving steps in their own words. We conducted a pilot study in which the tutor was used for two class periods in a junior high school. The data from this study suggest that the techniques that we chose to implement the dialogue system, namely a knowledge-based approach to natural language understanding and classification of student explanations, are up to the task. There are a number of ways in which the system could be improved within the current architecture.